Co-clinical trial targeting ER, FGFR and CDK4/6 in resistant hormone-positive breast cancer with FGFR alterations
Bibliographic record
Abstract
Management of advanced hormone receptor-positive, HER2-negative breast cancer after progression on endocrine therapy plus CDK4/6 inhibitors is challenging due to mutational heterogeneity. Current therapies yield limited efficacy, achieving 4-6 months PFS. FGFR signaling is implicated in resistance to endocrine plus CDK4/6 inhibitors, but FGFR inhibitors have shown limited activity in unselected populations. Co-clinical trials bridge preclinical and clinical findings, optimize resources, and enable biomarker identification. Using patient-derived organoids (PDOs), we demonstrated that FGFR-amplified PDOs respond to fulvestrant, palbociclib, and rogaratinib only when PIK3CA and ESR1 are wild-type. In a dose-escalation trial pre-screening 66 patients with FGFR1/2-amplification (FISH) and/or overexpression (RNAScope) patients, >40% harbored FGFR alterations. Nine patients were enrolled; the combination showed activity specifically in PIK3CA- and ESR1-wild type patients (9.1 vs. 1.9 months PFS; P = 0.0005). Toxicity was manageable and consistent with prior data. Our findings highlight biomarker-driven approaches as essential for refining FGFR-targeted strategies in this resistant population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".